A Unified State Space And Scenario Tree Framework For Multi Stage Stochastic Optimization

Download A Unified State Space And Scenario Tree Framework For Multi Stage Stochastic Optimization full books in PDF, epub, and Kindle. Read online A Unified State Space And Scenario Tree Framework For Multi Stage Stochastic Optimization ebook anywhere anytime directly on your device. Fast Download speed and no annoying ads. We cannot guarantee that every ebooks is available!

A Unified State-space and Scenario Tree Framework for Multi-stage Stochastic Optimization

Author : Steffen Rebennack
Publisher :
ISBN 13 :
Total Pages : pages
Book Rating : 4.:/5 (75 download)

DOWNLOAD NOW!

Book Synopsis A Unified State-space and Scenario Tree Framework for Multi-stage Stochastic Optimization by : Steffen Rebennack

Download or read book A Unified State-space and Scenario Tree Framework for Multi-stage Stochastic Optimization written by Steffen Rebennack and published by . This book was released on 2010 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: ABSTRACT: In the hydro-thermal scheduling problem, one is interested in determining the optimal operating policy for the use of hydro and thermal resources in order to minimize total expected costs of fulfilling the demand for electricity over a given time horizon. Originally proposed in 1991 by Pereira and Pinto, Stochastic Dual Dynamic Programming (SDDP) remains to date the most efficient algorithm which is able to cope with inflow uncertainty and a detailed representation of a system's characteristics. In this dissertation, we propose several extensions of the SDDP methodology: We embed the SDDP algorithm into a scenario tree framework, incorporate CO2 emission allowance constraints, and supplement the profit maximization models to account for CO2 emission allowance markets. These extensions allows us to additionally deal with uncertainties related to the evolution of demand and fuel prices. From a practical standpoint, this is an innovation as fuel price and electricity demand uncertainty could not be taken into account efficiently in hydro-thermal power systems so far, and from a technical standpoint, this is a new approach unifying the state-space and scenario tree framework. The importance of such an approach was made evident by the global economic crisis of 2008 when several countries experienced huge variations in demand and faced 13 sudden and sharp increases in fuel costs due to oil price swings, with implications not only on total incurred costs but also regarding security of supply.

A Scenario Tree-Based Decomposition for Solving Multistage Stochastic Programs

Author : Debora Mahlke
Publisher : Springer Science & Business Media
ISBN 13 : 3834898295
Total Pages : 194 pages
Book Rating : 4.8/5 (348 download)

DOWNLOAD NOW!

Book Synopsis A Scenario Tree-Based Decomposition for Solving Multistage Stochastic Programs by : Debora Mahlke

Download or read book A Scenario Tree-Based Decomposition for Solving Multistage Stochastic Programs written by Debora Mahlke and published by Springer Science & Business Media. This book was released on 2011-01-30 with total page 194 pages. Available in PDF, EPUB and Kindle. Book excerpt: Motivated by practical optimization problems occurring in energy systems with regenerative energy supply, Debora Mahlke formulates and analyzes multistage stochastic mixed-integer models. For their solution, the author proposes a novel decomposition approach which relies on the concept of splitting the underlying scenario tree into subtrees. Based on the formulated models from energy production, the algorithm is computationally investigated and the numerical results are discussed.

Reinforcement Learning and Stochastic Optimization

Author : Warren B. Powell
Publisher : John Wiley & Sons
ISBN 13 : 1119815037
Total Pages : 1090 pages
Book Rating : 4.1/5 (198 download)

DOWNLOAD NOW!

Book Synopsis Reinforcement Learning and Stochastic Optimization by : Warren B. Powell

Download or read book Reinforcement Learning and Stochastic Optimization written by Warren B. Powell and published by John Wiley & Sons. This book was released on 2022-03-15 with total page 1090 pages. Available in PDF, EPUB and Kindle. Book excerpt: REINFORCEMENT LEARNING AND STOCHASTIC OPTIMIZATION Clearing the jungle of stochastic optimization Sequential decision problems, which consist of “decision, information, decision, information,” are ubiquitous, spanning virtually every human activity ranging from business applications, health (personal and public health, and medical decision making), energy, the sciences, all fields of engineering, finance, and e-commerce. The diversity of applications attracted the attention of at least 15 distinct fields of research, using eight distinct notational systems which produced a vast array of analytical tools. A byproduct is that powerful tools developed in one community may be unknown to other communities. Reinforcement Learning and Stochastic Optimization offers a single canonical framework that can model any sequential decision problem using five core components: state variables, decision variables, exogenous information variables, transition function, and objective function. This book highlights twelve types of uncertainty that might enter any model and pulls together the diverse set of methods for making decisions, known as policies, into four fundamental classes that span every method suggested in the academic literature or used in practice. Reinforcement Learning and Stochastic Optimization is the first book to provide a balanced treatment of the different methods for modeling and solving sequential decision problems, following the style used by most books on machine learning, optimization, and simulation. The presentation is designed for readers with a course in probability and statistics, and an interest in modeling and applications. Linear programming is occasionally used for specific problem classes. The book is designed for readers who are new to the field, as well as those with some background in optimization under uncertainty. Throughout this book, readers will find references to over 100 different applications, spanning pure learning problems, dynamic resource allocation problems, general state-dependent problems, and hybrid learning/resource allocation problems such as those that arose in the COVID pandemic. There are 370 exercises, organized into seven groups, ranging from review questions, modeling, computation, problem solving, theory, programming exercises and a “diary problem” that a reader chooses at the beginning of the book, and which is used as a basis for questions throughout the rest of the book.

Multistage Stochastic Optimization

Author : Georg Ch. Pflug
Publisher : Springer
ISBN 13 : 3319088432
Total Pages : 309 pages
Book Rating : 4.3/5 (19 download)

DOWNLOAD NOW!

Book Synopsis Multistage Stochastic Optimization by : Georg Ch. Pflug

Download or read book Multistage Stochastic Optimization written by Georg Ch. Pflug and published by Springer. This book was released on 2014-11-12 with total page 309 pages. Available in PDF, EPUB and Kindle. Book excerpt: Multistage stochastic optimization problems appear in many ways in finance, insurance, energy production and trading, logistics and transportation, among other areas. They describe decision situations under uncertainty and with a longer planning horizon. This book contains a comprehensive treatment of today’s state of the art in multistage stochastic optimization. It covers the mathematical backgrounds of approximation theory as well as numerous practical algorithms and examples for the generation and handling of scenario trees. A special emphasis is put on estimation and bounding of the modeling error using novel distance concepts, on time consistency and the role of model ambiguity in the decision process. An extensive treatment of examples from electricity production, asset liability management and inventory control concludes the book.

A New Scenario-tree Generation Approach for Multistage Stochastic Programming Problems Based on a Demerit Criterion

Author : Julien Keutchayan
Publisher :
ISBN 13 :
Total Pages : 25 pages
Book Rating : 4.:/5 (13 download)

DOWNLOAD NOW!

Book Synopsis A New Scenario-tree Generation Approach for Multistage Stochastic Programming Problems Based on a Demerit Criterion by : Julien Keutchayan

Download or read book A New Scenario-tree Generation Approach for Multistage Stochastic Programming Problems Based on a Demerit Criterion written by Julien Keutchayan and published by . This book was released on 2017 with total page 25 pages. Available in PDF, EPUB and Kindle. Book excerpt:

ECAI 2016

Author : G.A. Kaminka
Publisher : IOS Press
ISBN 13 : 1614996725
Total Pages : 1860 pages
Book Rating : 4.6/5 (149 download)

DOWNLOAD NOW!

Book Synopsis ECAI 2016 by : G.A. Kaminka

Download or read book ECAI 2016 written by G.A. Kaminka and published by IOS Press. This book was released on 2016-08-24 with total page 1860 pages. Available in PDF, EPUB and Kindle. Book excerpt: Artificial Intelligence continues to be one of the most exciting and fast-developing fields of computer science. This book presents the 177 long papers and 123 short papers accepted for ECAI 2016, the latest edition of the biennial European Conference on Artificial Intelligence, Europe’s premier venue for presenting scientific results in AI. The conference was held in The Hague, the Netherlands, from August 29 to September 2, 2016. ECAI 2016 also incorporated the conference on Prestigious Applications of Intelligent Systems (PAIS) 2016, and the Starting AI Researcher Symposium (STAIRS). The papers from PAIS are included in this volume; the papers from STAIRS are published in a separate volume in the Frontiers in Artificial Intelligence and Applications (FAIA) series. Organized by the European Association for Artificial Intelligence (EurAI) and the Benelux Association for Artificial Intelligence (BNVKI), the ECAI conference provides an opportunity for researchers to present and hear about the very best research in contemporary AI. This proceedings will be of interest to all those seeking an overview of the very latest innovations and developments in this field.

A Scenario Generation Algorithm for Multistage Stochastic Programming: Application for Asset Allocation Models with Derivatives

Author :
Publisher :
ISBN 13 :
Total Pages : pages
Book Rating : 4.:/5 (631 download)

DOWNLOAD NOW!

Book Synopsis A Scenario Generation Algorithm for Multistage Stochastic Programming: Application for Asset Allocation Models with Derivatives by :

Download or read book A Scenario Generation Algorithm for Multistage Stochastic Programming: Application for Asset Allocation Models with Derivatives written by and published by . This book was released on with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: Modern financial portfolio management problems as well as asset/liability problems use stochastic optimization to allocate financial assets. To implement and solve such a stochastic optimization based portfolio allocation problem, we require scenario trees for the description of the future market evolutions of every random variable present in the model. This thesis proposes a general algorithm to construct scenario trees for underlying assets as well as options on these assets. The algorithm is based on the simulation of GARCH processes and on a Wasserstein distance minimization for the reduction of the number of scenarios. Several processes are analyzed, and empirical results on the DAX 100 and on European Put and Call options on this index are presented.

Barycentric Scenario Trees in Convex Multistage Stochastic Programming

Author : Karl Frauendorfer
Publisher :
ISBN 13 :
Total Pages : pages
Book Rating : 4.:/5 (611 download)

DOWNLOAD NOW!

Book Synopsis Barycentric Scenario Trees in Convex Multistage Stochastic Programming by : Karl Frauendorfer

Download or read book Barycentric Scenario Trees in Convex Multistage Stochastic Programming written by Karl Frauendorfer and published by . This book was released on 1996 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt:

Applications of Stochastic Programming

Author : Stein W. Wallace
Publisher : SIAM
ISBN 13 : 9780898718799
Total Pages : 724 pages
Book Rating : 4.7/5 (187 download)

DOWNLOAD NOW!

Book Synopsis Applications of Stochastic Programming by : Stein W. Wallace

Download or read book Applications of Stochastic Programming written by Stein W. Wallace and published by SIAM. This book was released on 2005-01-01 with total page 724 pages. Available in PDF, EPUB and Kindle. Book excerpt: Consisting of two parts, this book presents papers describing publicly available stochastic programming systems that are operational. It presents a diverse collection of application papers in areas such as production, supply chain and scheduling, gaming, environmental and pollution control, financial modeling, telecommunications, and electricity.

Lectures on Stochastic Programming

Author : Alexander Shapiro
Publisher : SIAM
ISBN 13 : 0898718759
Total Pages : 447 pages
Book Rating : 4.8/5 (987 download)

DOWNLOAD NOW!

Book Synopsis Lectures on Stochastic Programming by : Alexander Shapiro

Download or read book Lectures on Stochastic Programming written by Alexander Shapiro and published by SIAM. This book was released on 2009-01-01 with total page 447 pages. Available in PDF, EPUB and Kindle. Book excerpt: Optimization problems involving stochastic models occur in almost all areas of science and engineering, such as telecommunications, medicine, and finance. Their existence compels a need for rigorous ways of formulating, analyzing, and solving such problems. This book focuses on optimization problems involving uncertain parameters and covers the theoretical foundations and recent advances in areas where stochastic models are available. Readers will find coverage of the basic concepts of modeling these problems, including recourse actions and the nonanticipativity principle. The book also includes the theory of two-stage and multistage stochastic programming problems; the current state of the theory on chance (probabilistic) constraints, including the structure of the problems, optimality theory, and duality; and statistical inference in and risk-averse approaches to stochastic programming.

Scenario Tree Reduction for Multistage Stochastic Programs

Author : Holger Heitsch
Publisher :
ISBN 13 :
Total Pages : pages
Book Rating : 4.:/5 (118 download)

DOWNLOAD NOW!

Book Synopsis Scenario Tree Reduction for Multistage Stochastic Programs by : Holger Heitsch

Download or read book Scenario Tree Reduction for Multistage Stochastic Programs written by Holger Heitsch and published by . This book was released on 2008 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt:

Scenario Tree Modelling for Multistage Stochastic Programs

Author : Holger Heitsch
Publisher :
ISBN 13 :
Total Pages : 28 pages
Book Rating : 4.:/5 (951 download)

DOWNLOAD NOW!

Book Synopsis Scenario Tree Modelling for Multistage Stochastic Programs by : Holger Heitsch

Download or read book Scenario Tree Modelling for Multistage Stochastic Programs written by Holger Heitsch and published by . This book was released on 2005 with total page 28 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Scenario Optimization for Multi-stage Stochastic Programming Problems

Author :
Publisher :
ISBN 13 :
Total Pages : pages
Book Rating : 4.:/5 (118 download)

DOWNLOAD NOW!

Book Synopsis Scenario Optimization for Multi-stage Stochastic Programming Problems by :

Download or read book Scenario Optimization for Multi-stage Stochastic Programming Problems written by and published by . This book was released on 2005 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt:

Combining Stochastic Programming and Optimal Control to Solve Multistage Stochastic Optimization Problems

Author : Diana Barro
Publisher :
ISBN 13 :
Total Pages : 0 pages
Book Rating : 4.:/5 (137 download)

DOWNLOAD NOW!

Book Synopsis Combining Stochastic Programming and Optimal Control to Solve Multistage Stochastic Optimization Problems by : Diana Barro

Download or read book Combining Stochastic Programming and Optimal Control to Solve Multistage Stochastic Optimization Problems written by Diana Barro and published by . This book was released on 2012 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: In this contribution we propose an approach to solve a multistage stochastic programming problem which allows us to obtain a time and nodal decomposition of the original problem. This double decomposition is achieved applying a discrete time optimal control formulation to the original stochastic programming problem in arborescent form. Combining the arborescent formulation of the problem with the point of view of the optimal control theory naturally gives as a first result the time decomposability of the optimality conditions, which can be organized according to the terminology and structure of a discrete time optimal control problem into the systems of equation for the state and adjoint variables dynamics and the optimality conditions for the generalized Hamiltonian. Moreover these conditions, due to the arborescent formulation of the stochastic programming problem, further decompose with respect to the nodes in the event tree. The optimal solution is obtained by solving small decomposed subproblems and using a mean valued fixed-point iterative scheme to combine them. To enhance the convergence we suggest an optimization step where the weights are chosen in an optimal way at each iteration.

Approximate Dynamic Programming

Author : Warren B. Powell
Publisher : John Wiley & Sons
ISBN 13 : 0470182954
Total Pages : 487 pages
Book Rating : 4.4/5 (71 download)

DOWNLOAD NOW!

Book Synopsis Approximate Dynamic Programming by : Warren B. Powell

Download or read book Approximate Dynamic Programming written by Warren B. Powell and published by John Wiley & Sons. This book was released on 2007-10-05 with total page 487 pages. Available in PDF, EPUB and Kindle. Book excerpt: A complete and accessible introduction to the real-world applications of approximate dynamic programming With the growing levels of sophistication in modern-day operations, it is vital for practitioners to understand how to approach, model, and solve complex industrial problems. Approximate Dynamic Programming is a result of the author's decades of experience working in large industrial settings to develop practical and high-quality solutions to problems that involve making decisions in the presence of uncertainty. This groundbreaking book uniquely integrates four distinct disciplines—Markov design processes, mathematical programming, simulation, and statistics—to demonstrate how to successfully model and solve a wide range of real-life problems using the techniques of approximate dynamic programming (ADP). The reader is introduced to the three curses of dimensionality that impact complex problems and is also shown how the post-decision state variable allows for the use of classical algorithmic strategies from operations research to treat complex stochastic optimization problems. Designed as an introduction and assuming no prior training in dynamic programming of any form, Approximate Dynamic Programming contains dozens of algorithms that are intended to serve as a starting point in the design of practical solutions for real problems. The book provides detailed coverage of implementation challenges including: modeling complex sequential decision processes under uncertainty, identifying robust policies, designing and estimating value function approximations, choosing effective stepsize rules, and resolving convergence issues. With a focus on modeling and algorithms in conjunction with the language of mainstream operations research, artificial intelligence, and control theory, Approximate Dynamic Programming: Models complex, high-dimensional problems in a natural and practical way, which draws on years of industrial projects Introduces and emphasizes the power of estimating a value function around the post-decision state, allowing solution algorithms to be broken down into three fundamental steps: classical simulation, classical optimization, and classical statistics Presents a thorough discussion of recursive estimation, including fundamental theory and a number of issues that arise in the development of practical algorithms Offers a variety of methods for approximating dynamic programs that have appeared in previous literature, but that have never been presented in the coherent format of a book Motivated by examples from modern-day operations research, Approximate Dynamic Programming is an accessible introduction to dynamic modeling and is also a valuable guide for the development of high-quality solutions to problems that exist in operations research and engineering. The clear and precise presentation of the material makes this an appropriate text for advanced undergraduate and beginning graduate courses, while also serving as a reference for researchers and practitioners. A companion Web site is available for readers, which includes additional exercises, solutions to exercises, and data sets to reinforce the book's main concepts.

Planning Algorithms

Author : Steven M. LaValle
Publisher : Cambridge University Press
ISBN 13 : 9780521862059
Total Pages : 844 pages
Book Rating : 4.8/5 (62 download)

DOWNLOAD NOW!

Book Synopsis Planning Algorithms by : Steven M. LaValle

Download or read book Planning Algorithms written by Steven M. LaValle and published by Cambridge University Press. This book was released on 2006-05-29 with total page 844 pages. Available in PDF, EPUB and Kindle. Book excerpt: Planning algorithms are impacting technical disciplines and industries around the world, including robotics, computer-aided design, manufacturing, computer graphics, aerospace applications, drug design, and protein folding. Written for computer scientists and engineers with interests in artificial intelligence, robotics, or control theory, this is the only book on this topic that tightly integrates a vast body of literature from several fields into a coherent source for teaching and reference in a wide variety of applications. Difficult mathematical material is explained through hundreds of examples and illustrations.

Aimms Optimization Modeling

Author : Johannes Bisschop
Publisher : Lulu.com
ISBN 13 : 1847539122
Total Pages : 318 pages
Book Rating : 4.8/5 (475 download)

DOWNLOAD NOW!

Book Synopsis Aimms Optimization Modeling by : Johannes Bisschop

Download or read book Aimms Optimization Modeling written by Johannes Bisschop and published by Lulu.com. This book was released on 2006 with total page 318 pages. Available in PDF, EPUB and Kindle. Book excerpt: The AIMMS Optimization Modeling book provides not only an introduction to modeling but also a suite of worked examples. It is aimed at users who are new to modeling and those who have limited modeling experience. Both the basic concepts of optimization modeling and more advanced modeling techniques are discussed. The Optimization Modeling book is AIMMS version independent.